





"""simple docstring"""





from __future__ import annotations

import math


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list				,		SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) != 2 or len(a[0]     ) != 2 or len(SCREAMING_SNAKE_CASE     ) != 2 or len(b[0]     ) != 2:
            raise Exception("""Matrices are not 2x2"""     )
      lowerCAmelCase    		=			[
          [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
          [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
      ]
      return new_matrix


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list				,		SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      return [
          [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]     )     )]
          for row in range(len(SCREAMING_SNAKE_CASE     )     )
      ]


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list				,		SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      return [
          [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]     )     )]
          for row in range(len(SCREAMING_SNAKE_CASE     )     )
      ]


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) % 2 != 0 or len(a[0]     ) % 2 != 0:
            raise Exception("""Odd matrices are not supported!"""     )

      lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			matrix_length // 2

      lowerCAmelCase    		=			[[a[i][j] for j in range(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )] for i in range(SCREAMING_SNAKE_CASE     )]
      lowerCAmelCase    		=			[
          [a[i][j] for j in range(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )] for i in range(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      ]

      lowerCAmelCase    		=			[[a[i][j] for j in range(SCREAMING_SNAKE_CASE     )] for i in range(SCREAMING_SNAKE_CASE     )]
      lowerCAmelCase    		=			[[a[i][j] for j in range(SCREAMING_SNAKE_CASE     )] for i in range(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )]

      return top_left, top_right, bot_left, bot_right


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      return len(SCREAMING_SNAKE_CASE     ), len(matrix[0]     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      print("""\n""".join(str(SCREAMING_SNAKE_CASE     ) for line in matrix     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list				,		SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      if matrix_dimensions(SCREAMING_SNAKE_CASE     ) == (2, 2):
            return default_matrix_multiplication(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			split_matrix(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			split_matrix(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			actual_strassen(SCREAMING_SNAKE_CASE				,		matrix_subtraction(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )
      lowerCAmelCase    		=			actual_strassen(matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			actual_strassen(matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			actual_strassen(SCREAMING_SNAKE_CASE				,		matrix_subtraction(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )
      lowerCAmelCase    		=			actual_strassen(matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )
      lowerCAmelCase    		=			actual_strassen(matrix_subtraction(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )
      lowerCAmelCase    		=			actual_strassen(matrix_subtraction(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )

      lowerCAmelCase    		=			matrix_addition(matrix_subtraction(matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		SCREAMING_SNAKE_CASE     )				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			matrix_subtraction(matrix_subtraction(matrix_addition(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		SCREAMING_SNAKE_CASE     )				,		SCREAMING_SNAKE_CASE     )

      # construct the new matrix from our 4 quadrants
      lowerCAmelCase    		=			[]
      for i in range(len(SCREAMING_SNAKE_CASE     )     ):
            new_matrix.append(top_left[i] + top_right[i]     )
      for i in range(len(SCREAMING_SNAKE_CASE     )     ):
            new_matrix.append(bot_left[i] + bot_right[i]     )
      return new_matrix


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		list				,		SCREAMING_SNAKE_CASE	:		list     ):







      '''simple docstring'''




      if matrix_dimensions(SCREAMING_SNAKE_CASE     )[1] != matrix_dimensions(SCREAMING_SNAKE_CASE     )[0]:
            lowerCAmelCase    		=			(
                """Unable to multiply these matrices, please check the dimensions.\n"""
                F'Matrix A: {matrixa}\n'
                F'Matrix B: {matrixa}'
            )
            raise Exception(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			matrix_dimensions(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			matrix_dimensions(SCREAMING_SNAKE_CASE     )

      if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
            return [matrixa, matrixa]

      lowerCAmelCase    		=			max(*SCREAMING_SNAKE_CASE				,		*SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			int(math.pow(2				,		math.ceil(math.loga(SCREAMING_SNAKE_CASE     )     )     )     )
      lowerCAmelCase    		=			matrixa
      lowerCAmelCase    		=			matrixa

      # Adding zeros to the matrices so that the arrays dimensions are the same and also
      # power of 2
      for i in range(0				,		SCREAMING_SNAKE_CASE     ):
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]				,		SCREAMING_SNAKE_CASE     ):
                        new_matrixa[i].append(0     )
            else:
                  new_matrixa.append([0] * maxim     )
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]				,		SCREAMING_SNAKE_CASE     ):
                        new_matrixa[i].append(0     )
            else:
                  new_matrixa.append([0] * maxim     )

      lowerCAmelCase    		=			actual_strassen(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # Removing the additional zeros
      for i in range(0				,		SCREAMING_SNAKE_CASE     ):
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]				,		SCREAMING_SNAKE_CASE     ):
                        final_matrix[i].pop()
            else:
                  final_matrix.pop()
      return final_matrix


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  [
           [2, 3, 4, 5],
           [6, 4, 3, 1],
           [2, 3, 6, 7],
           [3, 1, 2, 4],
           [2, 3, 4, 5],
           [6, 4, 3, 1],
           [2, 3, 6, 7],
           [3, 1, 2, 4],
           [2, 3, 4, 5],
           [6, 2, 3, 1],
       ]
       SCREAMING_SNAKE_CASE__   =  [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
       print(strassen(matrixa, matrixa))













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






